Average Ratings 5 Ratings

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Average Ratings 0 Ratings

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Description

Key Features: 1. Coding: COSTAQDA provides a robust coding platform that allows researchers to categorize and tag qualitative data efficiently. It supports various coding methods, including descriptive, narrative, in vivo, and emotion coding, making it adaptable to a wide range of research methodologies. 2. Ordering/Organizing: The software enables users to structure their coded data systematically. Researchers can organize codes into hierarchies, categories, or themes, which is crucial for understanding complex relationships within the data and preparing it for deeper analysis. 3. Theme Discovery: COSTAQDA includes advanced tools for identifying and extracting themes from coded data. This thematic analysis capability helps researchers uncover patterns and insights that are essential for drawing meaningful conclusions from qualitative studies. 4. Testing Analysis: COSTAQDA supports the validation of research findings through its Testing Analysis feature. It includes an Inter-Coder Reliability test using Cohen's Kappa, which ensures consistency and reduces bias in coding, particularly in collaborative research projects.

Description

At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system. The Iris.ai Researcher Workspace is a flexible tool suite that allows to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GitHub

Integrations

GitHub

Pricing Details

$80
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Global Centre for Academic Research

Founded

2019

Country

South Africa

Website

www.vleresearch.net

Vendor Details

Company Name

Iris.ai

Founded

2015

Country

Norway

Website

iris.ai/

Product Features

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

Product Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

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